Media targeting system and method

ABSTRACT

A media targeting system and method uses visual pattern recognition techniques in association with consumer transactions as the basis for building a targeting database, which is then later used for automated consumer identification and targeted advertising purposes. The invention solves the problem of generating a large scale, robust media targeting database without relying on active or passive participation by consumers. Also disclosed are other demographics estimation systems and methods which facilitate less expensive media targeting capabilities which can be used in conjunction with the more robust transaction associated method disclosed.

PRIORITY CLAIM

This patent application claims the benefit of the filing date of theU.S. Provisional Patent Application Ser. No. 60/529,044, filed Dec. 15,2003 and entitled MEDIA TARGETING SYSTEM AND METHOD, the entire contentsof which are hereby expressly incorporated by reference.

FIELD OF THE INVENTION

This invention relates to media targeting systems and, in particular, toa system for customizing the digital advertising that is displayed toviewers in public spaces based on using visual pattern recognitiontechniques in association with previous retail transactions as the basisfor building a targeting database and delivering targeted advertising.The field of invention is related to, and alternately referred to as“digital signage,” “dynamic signage,” and “narrowcasting.”

BACKGROUND OF THE INVENTION

Digital Signage is an emerging visual advertising medium which utilizesdigital displays deployed into public spaces, connected through a widearea network, which display visual advertising messages to individualswithin the visual range of the display (“local traffic”). Theadvertising media takes the form of digital files which are distributedelectronically over the network to the remote display system to be runon the display in accordance with some predetermined criteria.

Early implementations of Digital Signage used a simple media loop inwhich a number of still images (“media segments”) would be displayed inseries, each for a period of time, and the cycle would be continuouslyrepeated throughout the day. In this mode, the advertising medium tookon the same basic characteristics as traditional static posteradvertising except that the ads could be more readily distributed andmore highly multiplexed. In spite of these advantages, the display sitedid not increase the total media value sufficiently to overcome theincreased costs of deploying and maintaining the Digital Signs.

In response to this problem, there have been efforts to design “mediatargeting” systems which tailor the media segments more specifically tothe characteristics of the local traffic of a particular display at agiven moment, as opposed to running the same loop continuously on all ofthe displays. By doing so, the total media value of a display site couldbe raised; if the media targeting is sufficiently robust it could raisethe media value of the site enough to overcome the increased costs andthereby support a viable business model.

There are three basic classes of media targeting on a Digital Signagenetwork: 1) those based on the typical demographic characteristics ofconsumers in the vicinity of the sign with no additional real-timedemographics information (Average Demographic Profile), 2) those basedon an estimation of the real-time demographics information of consumersin the vicinity of the sign without the benefit of direct consumeridentification (Estimated Demographic Profile), and 3) those based onactual real-time consumer demographics information determined by somekind of direct consumer identification method (Actual DemographicProfile).

In general, Actual Demographics Profile systems are preferred in thatthey more accurately reflect the real-time consumer demographics profilein the vicinity of the sign. However, if an Actual Demographics Profilesystem is only able to identify a small percentage of the consumers inthe vicinity of the sign, then the usefulness of such a system isdiminished. Therefore, a robust media targeting system requires not onlyActual Demographic Profile capabilities, but the ability to identify asignificant percentage of the consumers in the vicinity of the sign.

While Actual Demographics Profile systems are preferred, any methodwhich provides improved demographics profiling capabilities is useful.In order to illustrate this point, consider a Digital Sign in a U.Slocation which had no additional demographics data associated with it.From an advertiser's perspective, it would be assumed to have theaverage demographics profile of the U.S population (typically stated asa probabilistic profile). Taking one possible demographics vector,household income, the sign could be modeled by the following table:

PERCENT DISTRIBUTION Total 100.0 Less than $10,000 9.5 $10,000 to$14,999 6.3 $15,000 to $19,999 6.3 $20,000 to $24,999 6.6 $25,000 to$29,999 6.4 $30,000 to $34,999 6.4 $35,000 to $39,999 5.9 $40,000 to$44,999 5.7 $45,000 to $49,999 5.0 $50,000 to $59,999 9.0 $60,000 to$74,999 10.4 $75,000 to $99,999 10.2 $100,000 to $124,999 5.2 $125,000to $149,999 2.5 $150,000 to $199,999 2.2 $200,000 or more 2.4

For an advertiser interested in consumers whose household income wasbetween $25,000 and $35,000, 12.8% of the actual impressions would be ofvalue (sum of “$25,000 to $29,999” and “$30,000 to $34,999” percentagevalues). Now, assume that the same Digital Sign was characterized ashaving 18.4% of the consumers meeting this description: thecorresponding value to this advertiser was just raised 43%.

The basic requirements for creating a robust media targeting systeminclude: 1) the ability to automatically identify in real-time someindividual characteristic of a significant percentage of the individualscomprising the local traffic which can be used to uniquely identify theconsumer, 2) the ability to associate the identified individuals withdemographics data of interest to advertisers, and 3) the ability todynamically display media segments based on the profile of the localtraffic at that time.

A number of known prior art methods of individual identification requireactive cooperation on the part of the person to be identified, such asretina scanning for secure area access or swiping a magneticidentification card in a reader. Obviously these technologies would beimpractical for use in unrestricted public spaces which represents themajority of the Digital Signage market.

Other known prior art methods of individual identification requirepassive cooperation on the part of the person to be identified, such aswhat is described in Boyd/U.S. Pat. No. 6,484,148, the disclosure ofwhich is herein incorporated by reference, wherein unique “signaturesignals” from wireless devices such as a cell phones carried by usersare captured, and then associated to the user through the user's accountinformation. The problem with this kind of identification system is thatit requires cooperation by the third party service provider who holdsthe account information of the user. Because of privacy concerns thisinformation would not likely be released without user consent, or if itwere, would not likely withstand public scrutiny. As a result, this kindof system would be limited to users who provide passive cooperation and“opt-in,” thereby limiting the pool of identifiable local traffic belowthe necessary threshold.

A number of known prior art methods use camera-based visual patternrecognition for individual identification. The state of the art in thisfield continues to improve the accuracy of the identification process,the ability to identify in real time from a field of multipleindividuals, and the ability to identify individuals at fartherdistances from the camera. All of these trends improve the potentialusefulness of visual pattern recognition as an individual identificationtechnology within the field of this invention. However, to date none ofthe prior art methods describe a media targeting system that caneffectively associate the individual identification with meaningfulconsumer profile information without active or passive cooperation onthe part of the user, thereby limiting the system's ability to develop arobust, large-scale database.

The present invention addresses the deficiencies in the prior art andfacilitates the development of a robust media targeting system by usingvisual pattern recognition in conjunction with transaction datacollected at the point of purchase.

To draw the distinction between the prior art in visual patternrecognition and this present invention more clearly, the presentinvention is focused specifically on identification for use inconjunction with a robust media targeting system. It uses visual patternrecognition at a retail point-of-purchase transaction point for initialassociation with the consumer and the consumer's profile information,and then uses the visual identification indices to deliver targetedadvertising on a Digital Signage network at any future time at locationsseparate from the initial retail point-of-purchase transaction point.

The present invention is therefore novel in its application of visualpattern recognition technology, and unique in its capabilities, in thatit addresses all of the requirements for developing a large scale robustmedia targeting system whereas prior art has not.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide a robust mediatargeting system and method that overcomes many of the disadvantages ofprior art arrangements.

It is another object of the present invention to provide a substantiallyautomated system and method for identifying consumer profile informationthrough the use of visual pattern recognition technologies inconjunction with retail point-of-purchase transactions.

It is another object of the invention to provide a system and method torapidly build a large-scale database of consumer profile data withvisual pattern recognition based indices.

It is another object of the invention to provide a system and method todeliver other useful targeting capabilities while the robust large-scaledatabase is being built.

It is another object of the invention to provide a media targetingsystem that delivers some additional commercial uses beyond increasingmedia value on a Digital Signage network, so as to further acceleratewidespread adoption of the technology.

It is a further object of the invention to provide a system and methodto deploy demographically-targeted advertising on digital networks whichdoes not require additional visual recognition hardware, so thatadvertiser momentum can be created during the buildup phase of the morerobust visual pattern recognition based targeting.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates block diagram of a system according to one embodimentof the present invention.

FIG. 2 illustrates block diagram of a system according to anotherembodiment of the present invention, showing wide area connectivity toexternal databases.

FIG. 3 illustrates block diagram of a system according to anotherembodiment of the present invention, showing an alternate configurationof the retail location hardware.

FIG. 4 illustrates block diagram of a system according to anotherembodiment of the present invention, showing an alternate configurationof the advertising location hardware.

FIG. 5 illustrates block diagram of a software program according toanother embodiment of the present invention, showing the development ofa staging database for use by a media scheduling program.

FIG. 6 illustrates block diagram of a software program according toanother embodiment of the present invention, showing the use of thestaging database by a media scheduling program for determining which adto run next.

FIG. 7 illustrates block diagram of the Multi-Level DemographicsTargeting System according to another embodiment of the presentinvention.

FIG. 8 illustrates block diagram of a method for estimating ademographics profile of visitors of a facility according to anotherembodiment of the present invention, whereby home addresses areestimated using a simple inverse relationship between distance andnumber of visitors.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENT

In the following description of the preferred embodiment of the presentinvention, the term “demographics” is used frequently. While this termis sometimes used within the media industry to describe a finite set ofcommonly used population characterization criteria (such as age, income,and race), the term as used herein in the broadest possible sense as in“any characteristics of human populations and population segments usedto identify consumer markets.”

FIG. 1 illustrates an Media Targeting System 100 according to oneembodiment of the invention which includes Visual Pattern RecognitionSystem (VPRS) 102, Transaction Monitoring System 103, and Point-of-SaleTerminal 104 located at the Retail Location 101; connected over a WideArea Network (WAN) communications network 105 to a separate DigitalSignage Advertising Location 106 is another Visual Pattern RecognitionSystem 107 which is in communication with the Advertising DeliverySystem 108.

The Media Targeting System 100 uses any number of available VPRStechnologies, typically of the face recognition class, to establishindividual identification of the consumer while they are conducting somekind of financial transaction at a Point-of-Sale (“POS”) Terminal 104within a Retail Location 101. The VPRS technology (102 and 107) must becapable of reasonably accurate levels of individual identificationwithin close proximity to the camera (as would be the case at VPRSlocation 102), as well as preferably more distant locations typical ofDigital Signage applications situated in larger common areas of afacility containing the Advertising Location 105.

The VPRS technology (102 and 107) should also be one that establishes anidentification profile without requiring an initial reference image, andis able to capture the identification information within a fraction of asecond. It would also be necessary to use technology which would allowfor capturing and comparing against a database of previously collectedidentifications using mainstream computing and storage capabilities, soas to allow identification and delivery of a targeted ad while the localtraffic is walking past by the Advertising Location 106. For thosefamiliar in the art it is known that several VPRS technologies existwhich meet this criteria: the primary variable being what percentage ofaccurate identifications are possible. Significant research continues tobe done in this area which will likely result in continued improvementof accuracy levels over time. For the purpose of the present invention,current VPRS technologies meet the minimal accuracy levels required forat least some percentage of the applications so as to make the inventioncommercially useful, and future improvements will therefore expand theusefulness rather than establish it.

Referring again to the system configuration at the Retail Location 101shown in FIG. 1, the VPRS 102 system is generally connected to anintermediary Transaction Monitoring System (TMS) 103, which is incommunication with the relevant portions of a traditional POS Terminal's104 data stream. The TMS 103 collects the identification data from theVPRS 102 sub-system and associates it with current transaction databeing generated at POS Terminal's 104.

The TMS 103 collects some or all of the information generated during thetransaction, and forwards this and the associated identificationinformation made by the VPRS 102 during the course of the transactionover a Wide WAN 105 to the Media Targeting Data Warehouse (MTDW) storagesystem 109 (as shown in FIG. 2). The MTDW 109 is typically a structuredrelational database of some kind, and is designed to collect transactiondata and other available demographic information on the consumers thathave been identified by the Media Targeting System 100. The MTDW 109database is typically indexed on the VPRS 102 identification value (forwhich there is a one-to-one correlation with each consumer representedin the database) for rapid insertions and searches.

When the transaction at POS Terminal 104 includes a payment by creditcard, check, customer loyalty, or ATM card where standard consumeridentification information is also included (such as name and address ordriver's license number), then the TMS 103 can forward this informationto the MTDW 109 database along with the other transaction data. This isparticularly relevant in that once an association is made between theVPRS 102 identification value and other standard identificationreference points, the MTDW 109 database can use other Third-PartyDatabases 110 to build more depth into the consumer profile (as shown inFIG. 2). For example, demographic data such as age and income range,which are very useful media targeting criteria, could be added to theMTDW 109 database using third-party databases containing thisinformation; this would be in addition to the transaction histories andshopping pattern information being generated directly by the MediaTargeting System 100. Individually, each of these are data types wouldbe of interest to advertisers when directing advertising out onto aDigital Signage network; however, having both types increases the mediavalue substantially.

Referring again to the system configuration at the Advertising Location106 shown in FIG. 1, the VPRS 107 sub-system is connected to theAdvertising Delivery System 108. The VPRS 107 is similar to the VPRS 102sub-system in the Retail Location 101, except that the camera istypically set for a more distant focal range since it would typically becovering a section of the common area of the facility, monitoringconsumer traffic as they walked passed the Advertising Delivery System108. The Advertising Delivery System 108 would be either a visual- oraudio-based advertising delivery system (or both) designed to deliverthe targeted ads to consumers within the visual or audio range of theAdvertising Delivery System 108. In some cases, this would be whilewalking past a stationary point where the Advertising Delivery System108 was installed; in other cases, the Advertising Delivery System 108would be located in areas where consumers typically loitered for longerperiods of time. In either event, the architecture of the system wouldbe similar, except that in some cases the window of time available toestablish the identification and deliver a targeted ad might be less.

Referring again to the WAN 105 connections shown in FIG. 1, the TMS 103sub-system would usually share the existing WAN 105 connection of thePOS Terminal 104, although it could also use a separate WAN connection.The VPRS 102 sub-system would typically be situated on a local bus, indirect communication with the TMS 103.

An alternate configuration of the Retail Location 101 hardware would bean integrated system as shown in FIG. 3, wherein the TMS 112 and VPRS113 sub-systems are embedded directly into the POS Terminal 111 hardwaresystem.

Similarly, FIG. 4 shows an alternate configuration of the AdvertisingLocation hardware. In this configuration, an Ad Management System (AMS)117 is inserted between the VPRS 116 sub-system and the AdvertisingDelivery System 118. The AMS serves a similar function to the TMS 103 inthe Retail Location system by facilitating media targeting functionalityas a retrofit to an existing ADS system, or as an add-on to an existingADS architecture.

Referring again to FIG. 2, after a period of time following deploymentof the Retail Location 101 equipment, the MTDW 109 database would havesome number of retail transactions stored, along with the associatedidentification values generated by the VPRS 102 system (shown in FIG.1). At the Advertising Location 106, any of the consumers previouslyidentified who came within range of the ADS 108 (shown in FIG. 1) wouldbe identified by the VPRS 107 (shown in FIG. 1).

FIG. 5 illustrates a computer program flowchart which would run on theAdvertising Location 106 system, in accordance with one embodiment ofthe invention. This section of the code would generate a list ofidentified consumers and their related targeting data during the currentad cycle, which would in turn be used by the media scheduling engine todetermine which ad to run next.

The first step 121 would be a loop comparing identification valuesgenerated by the VPRS (107 or 116) at the Advertising Location 106 fromconsumers who were within view of the VPRS (107 or 116) camera. For thepurpose of illustration, assume that the structure of the identificationvalue is an integer between 1 and 300 million. Each recognized facewould generate a unique number, which would be used during section 121of the program to compare against a database of numbers (typicallyindexed on this field) to determine if there the number is alreadycontained in the database.

A more complex VPRS identification system might have multiple indices onwhich the identification is made, and return a probability of a matchrather than a simple “yes/no” response. In this case, section 102 of theprogram would be searching the MTDW 109 database on more than one field,evaluating the probability based on the appropriate algorithms, andgenerating a match when the probability exceeded the level establishedby the Advertising Location Software Program 120.

In either case, section 121 of the program would continue to look forwhat was determined to be a “match” with a consumer identification entryalready in the MTDW 109 database. Because of the continuous monitoringnature of this loop, it would probably be more efficient to maintain alocal copy of the MTDW 109 identification values rather than run thelocally generated values over the network and have the computer systemadjacent to the MTDW 109 database conduct the searches. In addition, itmay be appropriate to have additional targeting related fields alsostored locally.

Once a match is made, the program exits the section 121 loop and movesto the code specified in section 122. In section 122, the program wouldcollect any relevant targeting related fields contained in the remoteMTDW 109 database for the identified consumer to a local database, alongwith any additional targeting related fields contained locally, to aStaging Database 123 (FIG. 2). The Staging Database 123 is designed fortemporary storage of all relevant data for consumers generating a matchduring the section 121 program. It continues to collect new entriesduring each ad cycle, and is flushed at the end of each ad cycle.

Referring to FIG. 6, a Media Scheduling Engine software program 124would determine which advertisement to run based on entries contained inthe Staging Database 123 and other business rules employed by thatprogram. On the initiation of a new ad cycle 125, the Staging Database123 would be accessed to determine if there were any entries in it, andwhether the identification time stamp suggests that the consumer isstill in range of the Advertising Delivery System 108. If not, thenwhatever ad would normally be run by the Media Scheduling Engine 124would be run during the upcoming ad cycle 127. If, on the other hand,the section 126 decision tree returns a positive response then the MediaScheduling Engine 124 would modify the upcoming ad to one based on thealgorithm represented in section 128. This algorithm 128 would use thefields in the Staging Database 123 and other business rules to determinethe most “appropriate” ad to run. Appropriateness in terms of mediatargeting would generally relate back to the largest incrementalincrease in ad value generated by the available ads to be run and thedemographic profile of the identified consumers in range of theAdvertising Delivery System 108 at that point in time.

In consideration of the aforementioned aspects of the present invention,it is clear that the Media Targeting System 100 is capable of generatinga large database of identifiable consumers if deployed into asufficiently large number of retail transaction points. Unlike targetingmethods described in prior art, the present invention does not requireactive or passive participation by the consumer during theidentification process, or during the consumer demographic associationprocesses that follow. These deficiencies in the prior art representsubstantial impediments to building a large-scale database.

Furthermore, given the fact that the current incremental cost structurefor adding this capability to the Retail Location 101 and theAdvertising Location 105 hardware is relatively low, the potential forcommercial success of the invention is improved. To the extent that thisincremental cost must be evaluated against the incremental advertisingrevenues capable of being generated by the Media Targeting System 100 asa result of the targetability of the media, any increase in value to thePoint-of-Sale Terminal 104 would improve these dynamics and thereforeaccelerate the proliferation of the VPRS 102 systems necessary to buildthe MTDW 109 database.

One possible byproduct of the identification system installed at thePoint-of-Sale Terminal 104 would be its use to decrease fraudulenttransactions. Clearly, fraud represents a substantial cost to theindustry and to the extent that a system like this one could be used todecrease fraud, there would be an opportunity to improve adoption of thesystem by the retailers. Without some incentive like this, the retailerswould have limited incentive to add the necessary hardware to thePoint-of-Sale Terminal 104, even if it were provided at no cost to them.

Another possible byproduct of the identification system installed at thePoint-of-Sale Terminal 104 would be its use as a platform for providingpublic-space identification and tracking of suspects for HomelandSecurity officials.

Because the Media Targeting System 100 uses Visual Pattern Recognitiontechnology to identify consumers, the system could also be used toprovide the retailer at the Point-of-Sale Terminal 104 real-timefeedback on the identification, if constructed properly.

While the VPRS-based Media Targeting System 100 represents exceptionallyfine demographics targeting capabilities in that it is able to recognizespecific individuals and therefore associate very specific demographicsto them. It also has parallel benefits in the areas of consumer frauddeterrence and Homeland Security, thereby allowing for acceleratedadoption by the market. However, in spite of these positive attributes,the system ultimately requires deploying new application-specifichardware and systems into the field in order to build the targetingdatabase.

Because of the fact that the advertising industry has historicallyresisted adoption of new media models and methods until they can absorblarge amounts of advertising dollars, the market introductory phase forthe present invention will likely require significant amounts of capitalin order to overcome this inertia. One method for reducing thisintroduction inertia is represented by the block diagram shown in FIG.7. The diagram shows three levels of demographics targeting, eachrequiring additional system components to facilitate while providingincremental improvement in the “granularity” of the demographics (theability to distinguish consumer-specific demographics as opposed toapplying the same demographic profiles to a group of consumers which areactual or estimated weighted averages of the sample population).

Level One (129) uses the U.S. Census Bureau's demographics databases togenerate a demographics profile for the consumers likely to be in rangeof an advertising device. Because of the fact that the Census databasecan use a home address, neighborhood, or other larger geographic area asan index to retrieve a variety of demographics data, any advertisingdevice which can associate home address (or neighborhood, city, county,state, ZIP code, etc.) of the consumer or consumers who are likely to bein range of it can be converted to “Demographics-Associated AdvertisingDevice.”

In Wachob/U.S. Pat. No. 5,155,591, the disclosure of which is hereinincorporated by reference, demographics data for the consumer isassociated with a TV receiver unit which is in turn used for targetedadvertising purposes; as such, this would be an example of a where a TVreceiver unit is used as a Demographics-Associated Advertising Device(“DA/AD”). This model for the targeting of advertising is fundamentallydifferent from the other more common methods for targeted advertising,such as Program Association in the case of broadcast TV (where thedemographics profile of the audience who typically watches a givenProgram is used) or Search Word Association on the Internet (wheresearch terms are used as the basis of establishing demographics ofinterest to the advertiser).

While U.S. Pat. No. 5,155,591 describes a particular kind ofDemographics-Associated Advertising Device, it does not describe methodsto build the demographics profile based on Census data. In Shaffer/U.S.Pat. No. 6,748,426, the disclosure of which is herein incorporated byreference, the inventor describes methods to build a demographicsprofile based on Census data and use it to convert a consumer'snetworked personal computer into a Demographics-Associated AdvertisingDevice. This model could be described as a Census-DevelopedDemographics-Associated Advertising Device (“CD/DA/AD”) of NetworkedPersonal Computers type.

In a pending patent application of the present inventor (document number20020165781), a media management system is described which facilitatesplanning, buying, and managing advertising campaigns on networks ofDemographics-Associated Advertising Devices. In one embodiment of thatinvention, the Media Management System allows a media planner/buyer toview multiple types of Demographics-Associated Advertising Devicenetworks within the same media planning interface simply by selectingany one of several such networks which are made available by the system.For example, the media type might be a network of personal computersusing systems described in U.S. Pat. No. 6,748,426. Alternatively, themedia type could be a demographically-targetable Digital Signage networkas represented by numerous examples within the 20020165781 application.In addition, multiple media types or all available media types cansimilarly be viewed and used for planning and buying media on, withinthe same media planning interface and in conjunction with the same adcampaign. As can be appreciated by one of ordinary skill in the art,this ability to plan and deploy ad campaigns across ad mediums which aretraditionally separated could create substantial campaign processingefficiencies and generally improve the correlation between theadvertiser's desired impressions and actual impressions generated duringa given ad campaign for a given budget.

It is therefore desirable to maximize the number and size of DA/ADnetworks so that the media planning system described in 20020165781 willgain commercial acceptance more readily. In particular, it would bebeneficial to identify and design other CD/DA/AD types of networksbeyond Networked Personal Computers as described in U.S. Pat. No.6,748,426, since the Census data and address association is readilyavailable and efficient to use in developing an initial demographicsprofile.

Subscriber-based advertising devices, including cable or satellite TVs,mobile phones and PDAs, and satellite radio systems are perhaps theeasiest to convert into a CD/DA/AD network: the subscriber's address isgenerally a part of the provider's database and can therefore be used todevelop the CD/DA/AD profile.

Other presently-envisioned networks do not have actual subscribers, butmay have other methods for determining the consumer's address that is inrange of the advertising device. One such network would be In-FlightEntertainment systems located in airline seat-backs; these areparticularly useful wherein the consumer's seat is pre-assigned andtherefore the consumer's addresses are available within the airline'sdatabase. In the case of unassigned seating in that the averagedemographic profile for the passenger group could be created using theentire set of home addresses.

Public-space advertising devices such as Digital Signage networksgenerally lack the ability to identify a subscriber and associate thehome address in order to build the CD/DA/AD profile. However, varyinglevels of accuracy can be achieved in estimating the home addresses ofthe target populations, depending on the Digital Signage environment.For example, most regional mall marketing organizations develop trafficprofiles which estimate the number of visitors to the mall and wherethose visitors come from (what percent from what city). This is done asa matter of course in order to optimize their own advertising campaignsand develop actionable profiles of their customer base.

In the absence of any formal customer profiling information, afirst-order estimation could be developed by assigning percentages oftraffic to each of a number of ever-wider radii around the facility, asshown in FIG. 8; the reasoning being that the number of visitors to agiven location are generally inversely proportional to the distance theywould need to travel from their home to get there. In this example,three radii are used: Radius X (132) which is estimated to include 50%of the total traffic to the facility housing the Digital Sign, Radius Y(133) which is estimated to include an additional 25% of the totaltraffic to the facility housing the Digital Sign, and Radius Z (133)which is estimated to include 100% of the total traffic to the facilityhousing the Digital Sign. The actual number of radii and estimatedpercentages would vary depending on what was known about the actualtraffic patterns. The demographics profile would be developed by aweighted average of the census data in each of those radii.

A more accurate estimation could be developed in retail locations wherethe Digital Signs are in the vicinity of payment transaction terminalsof some kind (e.g., cash registers), and where the retailer capturedconsumer address information during the course of at least somepercentage of the transactions. In many cases, this kind of informationis now captured and maintained by retailers on a regular basis,providing the ability to create a very accurate profile of consumeraddresses and, in turn, a demographics profile. This environment alsoallows for the ability to overlay a real-time element to the historicalaverage in that actual consumer-specific demographics can be created atthe time of the transaction. If the Digital Signage displays were inview of the transaction terminal, this could be very useful from atargeting perspective. Even if they were not, the real-time demographicsinformation could be used to identify patterns occurring during certaintimes of the day or in certain areas within the store (where multipleregisters were used); these patterns could then be incorporated into amore robust demographics profile model.

In all of these cases, the corresponding media distribution system (likethat described in patent application Document Number 20020165781 by thepresent inventor, now pending) would view the Digital Sign or consumerentertainment appliance as having the estimated (or actual) number ofidentical consumers in view at a particular point in time, each havingthe same estimated demographics profile. Where additional real-timedemographics profile data was available, the total profile of consumersin range of the advertising device (e.g., Digital Sign) could bemodified as appropriate. For example, if the Digital Signage systemrecognized 5 individuals in range at the transition point to the next adcycle and one was uniquely identified by a transaction currentlyoccurring at the register, the profile would be modeled as fourindividuals with the current default (average) profile and one with theactual profile determined by the register transaction activity.

Capturing actual traffic “head-count” information would also be usefulfrom an advertiser's perspective, even without additional demographicsinformation being captured. It is well known within the advertisingindustry that one of the greatest deficiencies of most traditionaladvertising mediums is their inability to accurately track actual viewerimpressions. There are currently numerous methods and technologiesdesigned for simple traffic counting in commercial facilities.

One relatively new commercial technology is demonstrated by AdvancedInterfaces, Inc. of State College, Pa. Using cameras and customizedvisual recognition software, the company's Media Intelligence solutioncan estimate the general demographics characteristics of individuals inthe field of view of the camera. These demographics include age range,gender, and race of the individual. In a preferred embodiment of thepresent invention, this kind of demographics estimation using real-timeimage analysis could be used as a second level (130) of the Multi-LevelDemographics Targeting System shown in FIG. 7.

The third level of the Multi-Level Demographics Targeting System shownin FIG. 7 is the Media Targeting System 100, which would provideindividual identification capabilities and more detailed demographicsinformation.

Other types of initial demographics association with visualidentification systems and methods may also be used. For example,instead of the visual identification system being located in conjunctionwith a retail transaction terminal, it might be done during a videophonesession between the consumer and a cooperating business; the visualidentification in this place would still take place in a similar manner,using visual pattern recognition technologies of some kind, but theassociation with the consumer-identifying data could be done byassociating the consumer's CallerID if available, or in conjunction witha transaction taking place between the consumer and the business . . .the two required components are the ability to visually identify theconsumer and the ability to identify some other actionable data aboutthe consumer (address, full name, credit card number, etc.).

Other types of secondary consumer identification systems and methods mayalso be used. For example, instead of the system utilizing POStransaction data, the visual identification system may be able toacquire wireless MAC addresses from a wireless mobile device carried bythe consumer, and could cross reference the MAC address to some otherpersonal identification information for the consumer.

The present invention addresses the deficiencies in prior art byutilizing a range of demographics estimation technologies and methods: abaseline estimation which can automatically generate demographicsprofiles based on correlation of U.S. Census Bureau data and knowninformation about the consumer traffic, an additional level of detailusing real-time image analysis technologies which require the additionof appropriate camera hardware and analysis software to the DigitalSign, and the third level of detailed demographics using systemsassociated with transaction terminals and appropriate cameras andsystems on the Digital Sign and network.

The present invention is therefore novel in its application ofdemographics estimation technologies, and unique in its capabilities, inthat it addresses the stated deficiencies in the prior art.

Although this invention has been illustrated by reference to specificembodiments, it will be apparent to those skilled in the art thatvarious changes and modifications may be made which clearly fall withinthe scope of the invention. The invention is intended to be protectedbroadly within the spirit and scope of the appended claims.

What is claimed is:
 1. A computer system estimating a demographiccharacteristics profile of consumers in the vicinity of a publiclylocated advertising device, wherein said advertising device isconfigured to communicate advertising messages to a plurality ofconsumers within the vicinity of said advertising device, said systemcomprising: a memory; a processor configured to: identify a physicaladdress of an advertising device; identify one or more censusdemographics characteristics associated with a first geographic area,the first geographic area associated with said physical address andstore said census demographics characteristics associated with the firstgeographic area as a first demographics profile in said memory; identifya second geographic area a predetermined distance from said physicaladdress; identify one or more census demographics characteristicsassociated with said second geographic area and store said censusdemographics characteristics associated with said second geographic areaas a second demographics profile in said memory; and combine said firstdemographics profile and said second demographics profile into acombined demographics profile; wherein the combination is a weightedaverage of the first and the second demographics profiles, wherein theweighting factor is not equal to one.
 2. The system of claim 1, whereinsaid first demographics profile carries a higher weighting than thesecond demographics profile.
 3. The system of claim 1, wherein saidprocessor is further configured to deliver targeted advertising to saidadvertising device, said targeted advertising being selected in partbased on the said combined demographics profile.
 4. The system of claim1, wherein said processor is further configured to: receive retailtransaction data relating to transactions occurring in the vicinity ofsaid advertising device, wherein said retail transaction data includesconsumer address information; identify one or more census demographiccharacteristics associated with said consumer address information andstore said census demographic characteristics associated with saidconsumer address information as a third demographics profile in thememory; combine said combined demographics profile and said thirddemographics profile to create a refined demographics profile, whereinsaid refined demographics profile is a weighted average of the combinedand the third demographics profiles.
 5. The system of claim 4, whereinsaid weighted average of the combined and third demographic profiles isbased in part on an estimate of the number of consumers in the vicinityof the said advertising device at the time of the said transaction. 6.The system of claim 1, wherein the processor is further configured to:estimate one or more real-time demographics characteristics of consumersin said vicinity of said advertising device and store said real-timedemographics characteristics as a third demographics profile in memory,said estimation system using visual pattern recognition techniques; andcombine said combined demographics profile and said third demographicsprofile into a refined demographics profile, wherein said refineddemographics profile is a weighted average of the combined and the thirddemographics profiles.
 7. The system of claim 6, wherein said weightedaverage of the combined and third demographic profiles is based in parton an estimate of the number of consumers in the vicinity of the saidadvertising device at the time of the said visual pattern recognition.8. The system of claim 1, wherein the processor is further configuredto: acquire a unique electronic serial number or MAC address of awireless mobile device in the vicinity of said advertising device;identify consumer address information associated with said wirelessmobile device; identify one or more census demographic characteristicsassociated with said consumer address information and store as thirddemographics profile in memory; and combine said combined demographicsprofile and said third demographics profile into a refined demographicsprofile, wherein said refined demographics profile is a weighted averageof the combined and the third demographics profiles.
 9. The system ofclaim 8, wherein said weighted average of the combined and thirddemographic profiles is based in part on an estimate of the number ofconsumers in the vicinity of the said advertising device at the time ofthe said MAC address acquisition.
 10. A method for estimating, by aprocessor of a computer system, a demographic characteristics profile ofconsumers in the vicinity of a publicly located advertising device,wherein said advertising device is configured to communicate advertisingmessages to a plurality of consumers within the vicinity of saidadvertising device, said method comprising: identifying, using aprocessor of a computer system, a physical address of an advertisingdevice; identifying, using said processor of said computer system, oneor more first census demographics characteristics associated with afirst geographic area, the first geographic area associated with saidphysical address storing said one or more first census demographicscharacteristics as a first demographics profile in a memory;identifying, using said processor of said computer system, a secondgeographic area a predetermined distance from said physical address;identifying, using said processor of said computer system, one or moresecond census demographics characteristics associated with said secondgeographic area; storing said one or more second census demographicscharacteristics as a second demographics profile in said memory; andcombining, using said processor of said computer system, said firstdemographics profile and said second demographics profile into acombined demographics profile, wherein the combination is a weightedaverage of the first and the second demographics profiles, wherein theweighting factor is not equal to one.
 11. The method of claim 10,further comprising delivering targeted advertising to said advertisingdevice, said targeted advertising being selected in part based on thesaid combined demographics profile.
 12. The method of claim 10, whereinthe first demographics profile carries a higher weighting than thesecond demographics profile.
 13. The method of claim 10, furthercomprising: receiving retail transaction data from transactionsoccurring in the vicinity of said advertising device, wherein saidretail transaction data includes consumer address information;identifying one or more third census demographic characteristics thatare associated with said consumer address information; storing said oneor more third census demographic characteristics as a third demographicsprofile in memory, and; combining said combined demographics profile andsaid third demographics profile into a refined demographics profile,wherein said refined demographics profile is a weighted average of thecombined and the third demographics profiles.
 14. The method of claim13, wherein said weighted average of the combined and third demographicprofiles is based in part on an estimate of the number of consumers inthe vicinity of the said advertising device at the time of the saidtransaction.
 15. The method of claim 10, further comprising: estimatingone or more real-time demographics characteristics of consumers invicinity of said advertising device, said estimation system using visualpattern recognition techniques; storing said one or more real-timedemographics characteristics as third demographics profile in memory,and; combining said combined demographics profile and said thirddemographics profile into a refined demographics profile, wherein saidrefined demographics profile is a weighted average of the combined andthe third demographics profiles.
 16. The method of claim 15, whereinsaid weighted average of the combined and third demographic profiles isbased in part on an estimate of the number of consumers in the vicinityof the said advertising device at the time of the said visual patternrecognition.
 17. The method of claim 10, further comprising: acquiring aunique Electronic Serial Number or MAC address of a wireless mobiledevice in the vicinity of said advertising device; identifying consumeraddress information associated with said wireless mobile device;identifying one or more third census demographic characteristics thatare associated with said consumer address information; storing said oneor more third census demographic characteristics as a third demographicsprofile in memory, and; combining said combined demographics profile andsaid third demographics profile into a refined demographics profile,wherein said refined demographics profile is a weighted average of thecombined and the third demographics profiles.
 18. The method of claim17, wherein said weighted average of the combined and third demographicprofiles is based in part on an estimate of the number of consumers inthe vicinity of the said advertising device at the time of the said MACaddress acquisition.